Deep Learning Based Gait Recognition Using Convolutional Neural Network in the COVID-19 Pandemic

Citation

Sayeed, Md. Shohel and Min, Pa Pa and Bari, Md Ahsanul (2022) Deep Learning Based Gait Recognition Using Convolutional Neural Network in the COVID-19 Pandemic. Emerging Science Journal, 6 (5). pp. 1086-1099. ISSN 2610-9182

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Abstract

Gait recognition is the behavioral biometric trait that tracks humans based on their walking motion. It has gained attention because of its non-invasive and unobtrusive behaviors and applicable to the different application area. In this paper, we target model-free gait recognition with the deep learning approach for the Muslim community in the COVID-19 pandemic. The different convolutional neural network architectures (CNN) are examined by using the spatio-temporal gait representation called Gait Energy Images (GEI). We explored both the identification and verification problems to determine the suitability of the proposed CNN frameworks. In gait recognition, the intraclass variation is larger than the inter-class variation because of the shooting view, the walking speed, the wearing condition, and so on. To tackle this challenge, the verification framework is more suitable for the 1:1 association of gait recognition. As for the verification problem, we implemented the Siamese network with the parallel CNN architecture. All the proposed methods are tested against the public gait datasets called OUISIR-LP and OUISIR-MVLP to determine the identification and verification performance in terms of recognition accuracy and error rate.

Item Type: Article
Uncontrolled Keywords: Deep Learning, Convolutional Neural Network, Gait Recognition, COVID-19 Pandemic
Subjects: Q Science > QP Physiology > QP351 Neurophysiology and Neuropsychology
Divisions: Faculty of Information Science and Technology (FIST)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 06 Oct 2022 04:21
Last Modified: 06 Oct 2022 04:21
URII: http://shdl.mmu.edu.my/id/eprint/10475

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